摘要
针对传统卷积神经网络(CNN)对弧齿锥齿轮箱的故障识别准确率不高这一问题,提出一种基于深度分离卷积神经网络(DCNN)和双向长短时记忆网络(Bi-LSTM)的弧齿锥齿轮箱智能故障诊断方法。首先,对原始信号进行小波阈值降噪处理,将降噪后的信号利用经验模态分解(EMD)算法进行了分解;然后,对分解出的本征模态函数(IMF)的各个分量进行峭度计算,选取峭度值最高的IMF分量重构成新的振动信号输入模型进行训练;之后,将振动信号重叠采样获得大量信号样本,将这些样本通过深度分离卷积神经网络从一维原始振动信号中自适应的提取空间特征信息,提取的特征进一步输入到双向长短时记忆网络,同时提取正、逆时域的振动信号,以更好的提取故障特征;同时,在深度分离卷积中加入了残差网络对数据特征进行了复利用,并对卷积核进行了深度分离,解决了深度模型的网络退化问题;最后,将特征信息输入到已经训练好的DCNN-Bi-LSTM模型中,对弧齿锥齿轮箱故障诊断识别。结果表明,该方法可以准确的识别齿轮箱故障,最高诊断准确率可达100%。并且,该方法比传统的卷积神经网络的准确率更高,抗噪能力更强,网络收敛速度更快,诊断结果更稳定。
To solve the problem that the traditional convolutional neural network(CNN)is not high in fault identification accuracy for spiral bevel gear box,an intelligent fault diagnosis method based on deep separation convolutional neural network(DCNN)and Bi-LSTM was proposed.Firstly,the original signal is denoised by wavelet threshold,and then decomposed by empirical mode decomposition(EMD)algorithm.Then,each component of the decomposed eigenmode function(IMF)is kurtosis calculated,and the IMF component with the highest kurtosis value is selected to construct a new vibration signal input model for training.After that,a large number of signal samples are obtained by overlapping the vibration signals,and the spatial feature information of these samples is adaptively extracted from the one-dimensional original vibration signals through the deep separation convolutional neural network.The extracted features are further input into the bidirectional long short-term memory network,and the forward and inverse time-domain vibration signals are extracted at the same time to better extract fault features.At the same time,the residual network is added to the deep separation convolution to reuse the data features,and the convolutional kernel is deeply separated to solve the network degradation problem of the deep model.Finally,the feature information is input into the trained DCNN-Bi-LSTM model to diagnose and identify the spiral bevel gear box fault.The results show that this method can accurately identify gearbox faults,and the highest diagnostic accuracy can reach 100%.Moreover,the proposed method has higher accuracy,stronger anti-noise ability,faster convergence rate and more stable diagnosis results than traditional convolutional neural networks.
作者
荀小伟
许昕
潘宏侠
Xun Xiaowei;Xu Xin;Pan Hongxia(School of Mechanical Engineering,North University of China,Taiyuan 030051,China;System Identification and Diagnosis Technology Research Institute,North University of China,Taiyuan 030051,China)
出处
《电子测量技术》
北大核心
2024年第10期48-55,共8页
Electronic Measurement Technology
基金
内燃机可靠性国家重点实验室基金(skler-201911)项目资助。
关键词
深度分离卷积
双向长短时记忆网络
残差网络
智能故障诊断
本征模态函数
振动信号
deep separation convolutional
bidirectional long short-term memory
residual network
intelligent fault diagnosis
intrinsic mode function
vibration signal
